Many works have tackled the problem of generating new protein structures, but have generally run into challenges with creating diverse yet realistic folds. Traditional approaches typically apply heuristics to assemble fragments of experimentally profiled proteins into structures [1]}, [2]}. This approach is limited by the boundaries of expert knowledge and available data. More recently, deep generative models have been proposed. However, due to the incredibly complex structure of proteins, these commonly do not directly generate protein structures, but rather constraints (such as pairwise distance between residues) that are heavily post-processed to obtain structures [3]}, [4]}. Not only does this add complexity to the design pipeline, but noise in these predicted constraints can also be compounded during post-processing, resulting in unrealistic structures – that is, if the constraints are at all satisfiable to begin with. Other generative models rely on complex equivariant network architectures or loss functions to learn to generate a 3D point cloud that describes a protein structure [5]}, [6]}, [7]}, [8]}. Such equivariant architectures can ensure that the probability density from which the protein structures are sampled is invariant under translation and rotation. However, translation- and rotation-equivariant architectures are often also symmetric under reflection, leading to violations of fundamental structural properties of proteins like chirality [6]}. Intuitively, this point cloud formulation is also quite detached from how proteins biologically fold – by twisting to adopt energetically favorable configurations [10]}, [11]}.
